Family income and cardiovascular disease risk in American adults

Socioeconomic status is an overlooked risk factor for cardiovascular disease (CVD). Low family income is a measure of socioeconomic status and may portend greater CVD risk. Therefore, we assessed the association of family income with cardiovascular risk factor and disease burden in American adults. This retrospective analysis included data from participants aged ≥ 20 years from the National Health and Nutrition Examination Survey (NHANES) cycles between 2005 and 2018. Family income to poverty ratio (PIR) was calculated by dividing family (or individual) income by poverty guidelines specific to the survey year and used as a measure of socioeconomic status. The association of PIR with the presence of cardiovascular risk factors and CVD as well as cardiac mortality and all-cause mortality was examined. We included 35,932 unweighted participants corresponding to 207,073,472 weighted, nationally representative participants. Participants with lower PIR were often female and more likely to belong to race/ethnic minorities (non-Hispanic Black, Mexican American, other Hispanic). In addition, they were less likely to be married/living with a partner, to attain college graduation or higher, or to have health insurance. In adjusted analyses, the prevalence odds of diabetes mellitus, hypertension, coronary artery disease (CAD), congestive heart failure (CHF), and stroke largely decreased in a step-wise manner from highest (≥ 5) to lowest PIR (< 1). In adjusted analysis, we also noted a mostly dose-dependent association of PIR with the risk of all-cause and cardiac mortality during a mean 5.7 and 5.8 years of follow up, respectively. Our study demonstrates a largely dose-dependent association of PIR with hypertension, diabetes mellitus, CHF, CAD and stroke prevalence as well as incident all-cause mortality and cardiac mortality in a nationally representative sample of American adults. Public policy efforts should be directed to alleviate these disparities to help improve cardiovascular outcomes in vulnerable groups with low family income.


Study population and study variables.
We included all participants ≥ 20 years of age who did not have missing information on PIR (unweighted n = 35,932). PIR was used as a measure of participant financial status. The Department of Health and Human Services poverty guidelines were used as the poverty measure to calculate this ratio. PIR was calculated by dividing family (or individual) income by the poverty guidelines specific to the survey year. The value was not computed if the respondent only reported income as < $20,000 or ≥ $20,000. Values at or above 5.00 were coded as 5.00 or more because of disclosure concerns. The values were not computed if the income data was missing. We converted the ratio to a categorial variable of 6 levels: PIR < 1 (for the lowest income group), 1-1.9, 2-2.9, 3-3.9, 4-4.9, ≥ 5 (for the highest income group).
We examined several variables routinely collected in NHANES. This included socio-demographic variables (age, gender, health insurance, level of education, marital status, citizenship status). We also examined smoking status and marijuana use of the participants. Cardiovascular and other chronic health conditions were defined by the participants' response to a series of questions as a part of the standardized questionnaire: CHF: "Has a doctor or other health professional ever told you that you had congestive heart failure?" 9 ; Stroke: "Has a doctor or other health professional ever told you that you had a stroke?"; CAD: "Has a doctor or other health professional ever told you that you had coronary heart disease?", "Has a doctor or other health professional ever told you that you had angina, also called angina pectoris?", "Has a doctor or other health professional ever told you that you had a heart attack (also called myocardial infarction). Measurements of height, weight, and blood pressure were performed following a standardized protocol. Body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared (kg/m2). Obesity was defined as BMI ≥ 30 kg/m2. Hypertension was defined as having systolic blood pressure level ≥ 130 mm Hg and/or diastolic blood pressure level ≥ 80 mm Hg or yes to any of the following question: "Has a doctor or other health professional ever told you that you had had hypertension, also called high blood pressure", "told on 2 or more different visits that you had hypertension, also called high blood pressure?", "Because of your high blood pressure/hypertension, have you ever been told to take prescribed medicine?", "Are you now taking prescribed medicine". Diabetes mellitus was defined as having a hemoglobin A1c level ≥ 6.5% or serum glucose level ≥ 200 mg/dl or yes to any of the following question "Other than during pregnancy, have you ever been told by a doctor or health professional that you have diabetes or sugar diabetes?", "Are you taking insulin now", "Are you now taking diabetic pills to lower blood sugar". Dyslipidemia was defined as total cholesterol level ≥ 240 mg/dl or a participant answering yes to the question "Have you ever been told by a doctor or other health professional that your blood cholesterol level was high?", "To lower your blood cholesterol, have you ever been told by a doctor or other health professional to take prescribed medicine?". Waist circumferences of 102 cm or more for men and 88 cm or more for women were considered high.
Outcomes. We studied the association of PIR with the presence of cardiovascular risk factors (hypertension, diabetes mellitus, obesity, dyslipidemia), CVD (stroke, CHF, CAD), cardiac mortality, and all-cause mortality at the longest duration of follow-up. We combined the NHANES data with the cause of death from probabilistically linked death certificate records provided by the National Center of Health Statistics from the National Death Index 10 . The public-use linked mortality file for 1999-2014 NHANES includes follow-up time and the underlying cause of death for NHANES adult participants through December 31, 2015. Hence, the mortality analysis was restricted to participants included until 2014 9 . Cardiac deaths were defined as those with International Classification of Diseases, 10th Revision (ICD-10) codes I00 to I09, I11, I13, and I20 to I51. As a secondary exploratory analysis, we sought to evaluate the temporal trends in cardiovascular risk factors and CVD by categories of PIR across NHANES cycles from 2005 to 2018. We used univariable Cox proportional-hazards models to assess the association of PIR with all-cause and cardiac mortality. PIR < 1 was used as the reference category. Hazard ratios (HRs) and 95% CIs were estimated. Follow-up began at the time of interview and ended on the date of death or December 31, 2015, whichever came first. We used multivariable Model 1 to adjust for age, sex and race/ethnicity. Model 2 was adjusted for all the variables in Model 1 plus citizenship status, marital status, education status and insurance status (yes or no). Model 3 was adjusted for all the variables in Model 2 plus diabetes mellitus, hypertension, smoking status, dyslipidemia and obesity. Model 4 was adjusted for all the variables in Model 3 plus CAD, stroke and CHF.
Using STATA 16.1 (StataCorp, College Station, TX) 11 , our analyses took into account the NHANES survey design complexity by incorporating sampling weights, primary sampling units, and strata. This allowed us to estimate population proportions, means, and regression coefficients using svy commands. Appropriate sampling weights for each analysis were used as designated and described in detail in the NHANES methodology handbook. Standard errors (SEs) were computed using Taylor series linearization. Two-sided p-values < 0.05 were considered statistically significant.

Association of income with prevalence of diabetes mellitus. On multivariable analysis adjusting
for age, race/ethnicity and sex (model 1), compared with individuals with PIR < 1, prevalence odds of diabetes were lower in participants with PIR 1-1.  Table 3). When used as a continuous variable, increasing PIR was associated with lower odds of diabetes mellitus (0.90 (0.88-0.93), p < 0.001) [Supplementary Table 2].

Discussion
In the nationally representative data from NHANES, we report several important findings regarding the association of income disparity and CVD in US adults. First, the lower family income subgroup had a higher proportion of women, race/ethnic minorities, and uninsured individuals. In addition, the lower family income subgroup had higher rates of current smoking, marijuana use, and had lower educational attainment compared with the high family income subgroup. Second, lower income was independently associated with higher odds of diabetes mellitus, hypertension, CAD, CHF, and stroke. Third, we observed that lower income was also independently associated with an increased risk of all-cause and cardiac mortality during follow-up. Income is one of the major social determinants of health. Income disparity has been studied as a component of SES among NHANES participants in two recent studies. Zhang and colleagues studied NHANES III participants recruited in 1988-1994 and created a cumulative social risk score comprising of PIR < 1, minority race, education < 12th grade, and living single 12 . They observed that a higher cumulative social risk score was independently associated with 19% to 52% higher risk of CVD death 12 . Zhang et al. subsequently studied NHANES participants recruited in 1988-1994 (NHANES III) and 1999-2014 (continuous cycles) 13 . They quantified SES using a latent class analysis comprising of PIR, occupation or employment status, education level, and health insurance 13 . They observed that low SES was associated with more than two-fold higher risk of all-cause and CVD-related mortality in the study population 13 .
We observed a consistent dose-dependent association of PIR with prevalent CAD, CHF, and stroke. This relationship was independent of multiple relevant confounders including age, sex, race/ethnicity, cardiovascular risk factors, and importantly four other SES markers -educational attainment, marital status, citizenship status, and health insurance. We noted a similar association of PIR with hypertension and diabetes mellitus. Increased CVD prevalence in the lower income population is likely multifactorial. Social factors including food insecurity, lifestyle factors, lack of appropriate housing/transportation, lack of education/health literacy, inability to afford medications, decreased access to preventative health care, increased prevalence/poorer control of traditional risk factors have been implicated in causing poor health outcomes in low socioeconomic groups [14][15][16][17] . Higher CVD prevalence in lower income strata may also attributed to psychosocial stressors and coping behaviors such as drug or alcohol abuse. Table 4. Hazard ratios (95% confidence intervals) for the risk of all-cause mortality stratified by PIR Categories. *Models are adjusted for age, sex and race/ethnicity. # Models are adjusted for age, sex, race/ ethnicity, citizen status, marital status, education status and insurance (yes or no, not the types). $ Models are adjusted for age, sex, race/ethnicity, citizen status, marital status, education status and insurance (yes or no, not the types), diabetes mellitus, hypertension, smoker, obesity, dyslipidemia. ¶ Models are adjusted for age, sex, race/ethnicity, citizen status, marital status, education status and insurance (yes or no, not the types), diabetes mellitus, hypertension, smoker, obesity, dyslipidemia, CAD, CHF and stroke. www.nature.com/scientificreports/ Our study corroborates the findings of prior studies which have also documented the relationship between lower SES and cardiovascular morbidity and mortality [18][19][20][21][22][23] . In a recent seminal study, He et al. recently reported persistent income related disparities among participants of NHANES 7 . They estimated 10-year atherosclerotic CVD risk was significantly higher in the low-income group (PIR < 1) 7 . Abdalla et al. studied participants from nine NHANES cycles between 2009 and 2016, and stratified the study population into two groups using PIR cut-off of 5 24 . They studied the association of income disparity with prevalent CVD and observed that individuals with PIR ≥ 5 had lower prevalence of CVD as compared with those with PIR < 5 24 . This income related disparity remained consistent over time. In contrast to this study, we have studied PIR as a continuous as well as multi-level categorical variable in our analysis and have shown dose-dependent relationship of poverty with CVD prevalence.
We also extend the association of income and CVD to a consistent dose-dependent association between PIR and mortality risk. Kucharska-Newton et al. studied participants of the Atherosclerosis Risk in Communities (ARIC) study recruited in 1987-1989 and observed that low income (< $15,999) was associated with increased risk of sudden and non-sudden cardiac death, and nonfatal myocardial infarction among women, and with increased risk of sudden and non-sudden cardiac death among ARIC men 25 . Similarly, Elfassy and colleagues observed that income volatility and more than 25% income drop were independently associated with increased risk of incident CVD and mortality among Coronary Artery Risk Development in Young Adults (CARDIA) participants 26 . Faselis et al. studied Cardiovascular Health Study (CHS) participants recruited in 1989-1993 and reported that low income (< $16,000) was associated with 16% higher CVD risk and 19% higher mortality risk during follow-up 27 . Our study builds upon existing data by showing that these income related disparities have persisted well into the twenty-first century.
Our study has several important implications. The findings of our study highlight greater prevalence of CVD risk factors, CVD, and mortality among lower income households. Clinicians, health systems, payors, policymakers, and other relevant stakeholders should devise targeted interventions to achieve equity in cardiovascular healthcare and outcomes across the spectrum of income-strata. Essential strategies include improving healthcare access, promoting health education, improving housing and food quality, alleviating poverty, and other widespread public health and policy efforts for integrating social determinants of health (SDOH) into clinical care to help clinicians provide targeted care to marginalized populations. Further investigation is critical to determine the factors responsible for deleterious effects of family income on CVD prevalence. Research with robust study designs should focus on identifying the risk factors, causative mechanisms and systematic differences resulting in the disparate healthcare outcomes among different income strata.